Advertisement

Wireless Personal Communications

, Volume 95, Issue 3, pp 2049–2072 | Cite as

The Real-Time Detection and Prediction Method for Ballistic Aircraft Based on Distributed Sensor Networks

  • Lejiang GuoEmail author
  • Hao Li
  • Fangxin Chen
  • YaHui Hu
Article
  • 126 Downloads

Abstract

Space-based detection satellite and remote detection phased array radar are important parts of space security detection system. The aircraft’s detection, tracking and parameter estimation, trajectory prediction with detection satellite and radar are primary problems to be solved in the early detection of ballistic aircraft. This paper mainly studies the simulation of aircraft’s trajectory in early detection phase, the establishment of a dynamic model of the active segment and trajectory simulation and the simulation to generate a multi-level trajectory vehicle’s trajectory data based on the estimation of the key parameters of the aircraft in the single star observing conditions, the trajectory forecast and radar observation conditions for aircraft tracking. Due to the incompleteness of measurements for the single-satellite detection and the bad convergence, this paper proposes a fired at the focal plane method based on the priori template, establish a complete formula derivation algorithm processes, establish a priori standard ballistic template with the simulation trajectory data and the validity of the method using Monte Carlo simulation. Based on the MATLAB graphical user interface, it builds a simulation platform of ballistics aircraft detection probe which can effectively complete the early detection of scene simulation and demonstration. The simulation results show that the method can solve the bad convergence problems of the detection of a single star and it suits for the application to the ballistic vehicle’s key point estimation.

Keywords

Ballistic simulation Target tracking Parameter estimation Interactive multiple models 

Notes

Acknowledgements

This work was supported by the Chinese National Natural Science Foundation (No. 60773190, 60802002).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

References

  1. 1.
    Steven, M. D., Malthus, T. J., Baret, F., Xu, H., & Chopping, M. J. (2003). Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment, 88, 412–422.CrossRefGoogle Scholar
  2. 2.
    Yang, X., & Lo, C. (2000). Relative radiometric normalization performance for change detection from multi-date satellite images. Photogrammetric Engineering and Remote Sensing, 66, 967–980.Google Scholar
  3. 3.
    Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing, 62, 1025–1035.Google Scholar
  4. 4.
    Albright, T. P., & Ode, D. J. (2011). Monitoring the dynamics of an invasive emergent macrophyte community using operational remote sensing data. Hydrobiologia, 661, 469–474.CrossRefGoogle Scholar
  5. 5.
    Silva, T. S. F., Costa, M. P. F., & Melack, J. M. (2010). Spatial and temporal variability of macrophyte cover and productivity in the eastern Amazon floodplain: A remote sensing approach. Remote Sensing of Environment, 114, 1998–2010.CrossRefGoogle Scholar
  6. 6.
    Tian, Y. Q., Yu, Q., Zimmerman, M. J., Flint, S., & Waldron, M. C. (2010). Differentiating aquatic plant communities in a entropic river using hyperspectral and multispectral remote sensing. Freshwater Biology, 55, 1658–1673.Google Scholar
  7. 7.
    Silva, T. S. F., Costa, M. P. F., Melack, J. M., & Novo, E. M. L. M. (2008). Remote sensing of aquatic vegetation: theory and applications. Environmental Monitoring and Assessment, 140, 131–145.CrossRefGoogle Scholar
  8. 8.
    Fitzgerald, R. J. (1974). On reentry vehicle tracking in various coordinate systems. IEEE Transactions on Automatic Control, AC-19, 581–582.CrossRefGoogle Scholar
  9. 9.
    Farina, A., Ristic, B., & Benvenuti, D. (2002). Tracking a ballistic target: Comparison of several filters. IEEE Transactions on Aerospace and Electronic Systems, 38(3), 1916–1924.CrossRefGoogle Scholar
  10. 10.
    Hough, M. E. (1999). Improved performance of recursive tracking filters using batch initialization and process noise adaptation. AIAA Journal of Guidance, Control and Dynamics, 22(5), 675–681.CrossRefGoogle Scholar
  11. 11.
    Chen, H., Bar-Shalom, Y., Pattipati, K. R., & Kirubarajan, T. (2003). MDL approach for multiple low observable track initiation. IEEE Transactions on Aerospace and Electronic Systems, 39(3), 862–882.CrossRefGoogle Scholar
  12. 12.
    Luo, X., Yang, X., Wang, W., Chang, X., Wang, X., & Zhao, Z. (2016). A novel hidden danger prediction method in cloud-based intelligent industrial production management using timeliness managing extreme learning machine. China Communications, 13(7), 74–82.CrossRefGoogle Scholar
  13. 13.
    Chang, C. B., Whiting, R. H., & Athans, M. (1977). On the state and parameter estimation for maneuvering reentry vehicles. IEEE Transactions Automatic Control, 22(2), 99–105.CrossRefGoogle Scholar
  14. 14.
    Cooperman, R. L. (2002) Tactical ballistic missile tracking using the interacting multiple model algorithm. In Proceedings of the 2002 International Conference on Information Fusion, Annapolis, MD, pp. 824–831.Google Scholar
  15. 15.
    Farina, A., Del Gaudio, M. G., D’Elia, U., Immediata, S., Ortenzi, L., Timmoneri, L., et al. (2004) Detection and tracking of ballistic target. In Proceedings of the 2004 IEEE international radar conference, pp. 450–456.Google Scholar
  16. 16.
    Qiu, Z., Ruan, J., Huang, D., Pu, Z., & Shu, S. (2015). A prediction method for breakdown voltage of typical air gaps based on electric field features and support vector machine. IEEE Transactions on Dielectrics and Electrical Insulation, 22(4), 2125–2135.CrossRefGoogle Scholar
  17. 17.
    Gallais, P. (2007). Atmospheric re-entry vehicle mechanics. New York: Springer.Google Scholar
  18. 18.
    Li, X. R., & Bar-Shalom, Y. (1994). A recursive multiple model approach to noise identification. IEEE Transactions of Aerospace and Electronic Systems, 30(3), 671–684.CrossRefGoogle Scholar
  19. 19.
    Sarikaya, R., & Buyuktosunoglu, A. (2010). A unified prediction method for predicting program behavior. IEEE Transactions on Computers, 59(2), 272–282.MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Sivananthan, S., Kirubarajan, T., & Bar-Shalom, Y. (2001). Radar power multiplier for acquisition of low observables using an ESA radar. IEEE Transactions on Aerospace and Electronic Systems, 37(2), 401–418.CrossRefGoogle Scholar
  21. 21.
    Ma, J., Zhang, G., & Lu, J. (2012). A method for multiple periodic factor prediction problems using complex fuzzy sets. IEEE Transactions on Fuzzy Systems, 20(1), 32–45.CrossRefGoogle Scholar
  22. 22.
    Sharma, Jayant, Stokes, Grant H., von Braun, Curt, et al. (2002). Toward operational space-based space surveillance. Lincoln Laboratory Journal, 13(2), 309–313.Google Scholar
  23. 23.
    Danis, N. J. (1993). Space-based tactical ballistic missile launch parameter estimation. IEEE Transaction on Aerospace and Electronic Systems, 29(2), 413–424.CrossRefGoogle Scholar
  24. 24.
    Shihui, W., & Longxu, X. (2008). Research on ballistic missile laser SIMU error propagation mechanism. Journal of Systems Engineering and Electronics, 19(2), 356–362.CrossRefzbMATHGoogle Scholar
  25. 25.
    Yong-jie, W. A. N. G. (2008). Research on probabilistic method of flight impact point for trouble missile with Monte Carlo method. Systems Engineering and Electronics, 30(4), 682–685.Google Scholar
  26. 26.
    Kantola, M., Perttunen, M., Leppanen, T., Collin, J., & Riekki, J. (2010). Context awareness for GPS-enabled phones. In Proceedings of ION technical meeting, Manassas, VA, USA, pp. 117–124.Google Scholar
  27. 27.
    Kavzoglu, T., & Mather, P. M. (2002). The role of feature section in artificial neural network applications. International Journal of Remote Sensing, 23, 2919–2937.CrossRefGoogle Scholar
  28. 28.
    Chen, L., & Fang, J. (2014). A hybrid prediction method for bridging GPS outages in high-precision POS application. IEEE Transactions on Instrumentation and Measurement, 63(6), 1656–1665.CrossRefGoogle Scholar
  29. 29.
    Luo, H., Li, W., & He, X. (2015). Online high-power P-i-N diode chip temperature extraction and prediction method with maximum recovery current di/dt. IEEE Transactions on Power Electronics, 30(5), 2395–2404.CrossRefGoogle Scholar
  30. 30.
    Gan, Y., Jiang, C., Beaulieu, N. C., Wang, J., & Ren, Yong. (2016). Secure collaborative spectrum sensing: A peer-prediction method. IEEE Transactions on Communications, 64(10), 4283–4294.Google Scholar
  31. 31.
    Turley, M. D. E. (2008). Signal processing techniques for maritime surveillance with skywave radar. In Proceedings of 2008 IEEE radar conference. Adelaide: IEEE Press, pp. 241–246.Google Scholar
  32. 32.
    Tang, Y. J. (2004) Ocean clutter suppression using one-class SVM. IEEE workshop on machine learning for signal processing.Google Scholar
  33. 33.
    You, R.-J., & Lin, B.-C. (2011). A quality prediction method for building model reconstruction using LiDAR data and topographic maps. IEEE Transactions on Geoscience and Remote Sensing, 49(9), 3471–3480.CrossRefGoogle Scholar
  34. 34.
    Chen, L., & Fang, J. (2014). A hybrid prediction method for bridging GPS outages in high-precision POS application. IEEE Transactions on Instrumentation and Measurement, 63(6), 1656–1665.CrossRefGoogle Scholar
  35. 35.
    Paunovic, D. S., Stojanovic, Z. D., & Stojanovic, I. S. (1984). Choice of a suitable method for the prediction of the field strength in planning land mobile radio systems. IEEE Transactions on Vehicular Technology, 33(3), 259–265.CrossRefGoogle Scholar
  36. 36.
    Houminer, Z., Russell, C. J., Dyson, P. L., et al. (1996). Study of sporadic-E clouds by backscatter radar. Annales Geophysicae, 14, 1060–1065.CrossRefGoogle Scholar
  37. 37.
    Zhao, J., Wang, W., Liu, Y., & Pedrycz, W. (2011). A two-stage online prediction method for a blast furnace gas system and its application. IEEE Transactions on Control Systems Technology, 19(3), 507–520.CrossRefGoogle Scholar
  38. 38.
    Wu, C. R., Lin, C. T., & Tsai, P. H. (2010). Evaluating business performance of wealth management banks. European Journal of Operational Research, 207(2), 971–979.CrossRefGoogle Scholar
  39. 39.
    Zaveri, M., Merchant, S. N., & Desai, U. B. (2007). Robust neural-network-based data association and multiple model-based tracking of multiple point targets. IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews (S1094-6977), 37(3), 337–351.CrossRefGoogle Scholar
  40. 40.
    Liu, Y. H., Nie, Z. P., & Zhao, Z. Q. (2009). Cascaded approach for correcting ionospheric frequency modulation in HF sky-wave radars. Journal of University of Electronic Science and Technology of China, 38(1), 17–20.Google Scholar
  41. 41.
    Liu, S., Sheng, W., Zhang, X., et al. (2012) Digital generating scheme of composite discrete chaotic biphase coded signals. EICE 2012, Macau, China, pp. 105–109.Google Scholar
  42. 42.
    Chen, H., & Chang, K. C. (2009). Novel nonlinear filtering and prediction method for maneuvering target tracking. IEEE Transactions on Aerospace and Electronic Systems, 45(1), 237–249.CrossRefGoogle Scholar
  43. 43.
    Zhang, X. H., Sheng, W., & Liu, S. H. (2013). Suppression of sea clutter of skywave radar based on AR model. Measurement technology and its application Part 1. Applied Mechanics and Materials, 239, 382–386.CrossRefGoogle Scholar
  44. 44.
    Conte, E., De, M. A., & Galdi, C. (2004). Statistical analysis of real clutter at different range resolutions. IEEE Transactions on Aerospace and Electronic Systems, 40(3), 903–918.CrossRefGoogle Scholar
  45. 45.
    Weeks, D. J. (2005). Small satellites and the DARPA/Air Force FALCON program. Acta Astronautica, 57(2), 469–477.CrossRefGoogle Scholar
  46. 46.
    Gordon, N. (1997). A hybrid bootstrap filter for target tracking in clutter. IEEE Transactions on Aerospace and Electronic Systems, 33(1), 353–358.MathSciNetCrossRefGoogle Scholar
  47. 47.
    Sachs, G. (2005). Longitudinal long-term modes in super-And hypersonic flight. Journal of Guidance, Control and Dynamics, 28(3), 539–540.CrossRefGoogle Scholar
  48. 48.
    Boutayeb, M., Rafaralahy, H., & Darouach, M. (1997). Convergence analysis of the extended Kalman filter used as an observer for nonlinear deterministic discrete-time systems. IEEE Transactions on Automatic Control, 42(4), 581–586.MathSciNetCrossRefzbMATHGoogle Scholar
  49. 49.
    Wei, H., Bin, L. S., Jun, L., & Guo, W. Z. (2010). Effect of cavity flame holder configuration on combustion flow field performance of integrated hypersonic vehicle. Science China Technological Sciences, 53(10), 2725–2733.CrossRefGoogle Scholar
  50. 50.
    Mohan, A., Papageorgiou, C., & Poggio, T. (2001). Example-based object detection in images by components [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(4), 349–351.CrossRefGoogle Scholar
  51. 51.
    Dalal, N., & Triggs, B. (2011). Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 886–887.Google Scholar
  52. 52.
    Marshall, L. A., & Bahm, C. (2005) Overview with results and lessons learned of the X-43A Mach 10 Flight. A collection of technical papers-13th AIAA/CIRA international space planes and hypersonic systems and technologies conference. Vol. 2, pp. 1237–1259.Google Scholar
  53. 53.
    Wen, C. Y., Chou, S., & Liaw, J. (2012). Textural defect segmentation using a fourier-domain maximum likelihood estimation method. Textile Research Journal, 72(3), 253–254.Google Scholar
  54. 54.
    Huang, Y., & Chan, K. (2010). Texture decomposition by harmonics extraction from higher order statistics. IEEE Transactions on Image Processing, 13(1), 3–4.Google Scholar
  55. 55.
    Zhenjun, Z., Zhiguo, C., & Wenwu, W. (2011). Effects of hypersonic vehicle’s optical dome on infrared imaging. Optical Engineering, 50(9), 119–124.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Early Warning Surveillance IntelligenceAir Force Early Warning AcademyWuhanChina
  2. 2.Equipment Development and Application Research CenterAir Force Engineering UniversityXi’anChina

Personalised recommendations